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June, 2017

Nvidia has released a new version of TensorRT, a runtime system for serving inferences using deep learning models through Nvidia’s own GPUs.

Inferences, or predictions made from a trained model, can be served from either CPUs or GPUs. Serving inferences from GPUs is part of Nvidia’s strategy to get greater adoption of its processors, countering what AMD is doing to break Nvidia’s stranglehold on the machine learning GPU market.

Nvidia claims the GPU-based TensorRT is better across the board for inferencing than CPU-only approaches. One of Nvidia’s proffered benchmarks, the AlexNet image classification test under the Caffe framework, claims TensorRT to be 42 times faster than a CPU-only version of the same test — 16,041 images per second vs. 374—when run on Nvidia’s Tesla P40 processor. (Always take industry benchmarks with a grain of salt.)

Hortonworks and IBM recently announced an expanded partnership. The deal pairs IBM’s Data Science Experience (DSX) analytics toolkit and the Hortonworks Data Platform (HDP), with the goal of extending machine learning and data science tools to developers across the Hadoop ecosystem. IBM’s Big SQL, a SQL engine for Hadoop, will be leveraged as well.

InfoWorld Editor at Large Paul Krill recently met with Hortonworks CEO Rob Bearden and IBM Analytics general manager Rob Thomas at the DataWorks Summit conference in Silicon Valley, to talk about the state of big data analytics, machine learning, and Hadoop’s standing among the expanding array of technologies available for large-scale data processing.

As the blockchain continues to mature and find adoption in areas other than cryptocurrency, ERP vendors are working to integrate the distributed ledger technology as a trackable, immutable record for everything from shipping manifests and supply cha…